gcp-patterns

name	gcp-patterns
description	Google Cloud Platform best practices for serverless, event-driven architectures

GCP Patterns Skill

Instructions for AI

Apply Google Cloud Platform best practices for serverless, event-driven architectures. Use this skill when designing, implementing, or reviewing GCP-based systems.

Core Service Patterns

1. Cloud Run API Service

# main.py — FastAPI on Cloud Run
from fastapi import FastAPI, Depends
from google.cloud import secretmanager

app = FastAPI(title="Order Service")

def get_secret(secret_id: str) -> str:
    client = secretmanager.SecretManagerServiceClient()
    name = f"projects/{PROJECT_ID}/secrets/{secret_id}/versions/latest"
    response = client.access_secret_version(request={"name": name})
    return response.payload.data.decode("UTF-8")

@app.get("/health")
async def health():
    return {"status": "healthy"}

2. Pub/Sub Event Processing

# Cloud Function triggered by Pub/Sub
import functions_framework
from cloudevents.http import CloudEvent
import json

@functions_framework.cloud_event
def process_order_event(cloud_event: CloudEvent) -> None:
    data = json.loads(base64.b64decode(cloud_event.data["message"]["data"]))
    order_id = data["order_id"]
    
    # Process with retry-safe idempotency
    if already_processed(order_id):
        return
    
    process_order(data)
    mark_processed(order_id)

3. BigQuery Analytics Pipeline

from google.cloud import bigquery

def load_to_bigquery(data: list[dict], table_id: str) -> None:
    client = bigquery.Client()
    job_config = bigquery.LoadJobConfig(
        write_disposition=bigquery.WriteDisposition.WRITE_APPEND,
        schema_update_options=[
            bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION,
        ],
    )
    job = client.load_table_from_json(data, table_id, job_config=job_config)
    job.result()  # Wait for completion

4. Terraform Module Pattern

# modules/pubsub-topic/main.tf
variable "topic_name" { type = string }
variable "project_id" { type = string }
variable "subscribers" {
  type = list(object({
    name     = string
    endpoint = string
  }))
  default = []
}

resource "google_pubsub_topic" "topic" {
  name    = var.topic_name
  project = var.project_id

  message_retention_duration = "86400s"
}

resource "google_pubsub_topic" "dead_letter" {
  name    = "${var.topic_name}-dlq"
  project = var.project_id

  message_retention_duration = "604800s"  # 7 days retention for dead letters
}

resource "google_pubsub_topic_iam_member" "dead_letter" {
  topic   = google_pubsub_topic.dead_letter.name
  role    = "roles/pubsub.publisher"
  member  = "serviceAccount:service-${data.google_project.project.number}@gcp-sa-pubsub.iam.gserviceaccount.com"
}

resource "google_pubsub_subscription" "sub" {
  for_each = { for s in var.subscribers : s.name => s }

  name  = each.value.name
  topic = google_pubsub_topic.topic.name

  push_config {
    push_endpoint = each.value.endpoint
  }

  dead_letter_policy {
    dead_letter_topic     = google_pubsub_topic.dead_letter.id
    max_delivery_attempts = 5
  }

  retry_policy {
    minimum_backoff = "10s"
    maximum_backoff = "600s"
  }
}

Architecture Patterns

Event-Driven Microservices on GCP

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Cloud Run   │────▶│   Pub/Sub   │────▶│ Cloud Run   │
│  Service A   │     │   Topic     │     │  Service B  │
└─────────────┘     └─────────────┘     └─────────────┘
                          │
                          ▼
                    ┌─────────────┐
                    │ Cloud Func  │
                    │ (Analytics) │
                    └──────┬──────┘
                           │
                           ▼
                    ┌─────────────┐
                    │  BigQuery   │
                    └─────────────┘

Security Layers

Layer	Service	Purpose
Edge	Cloud Armor	WAF, DDoS protection
Identity	Identity-Aware Proxy	Authentication
Network	VPC / Private Google Access	Network isolation
Secrets	Secret Manager	Credential storage
IAM	Service Accounts	Least-privilege access
Encryption	Cloud KMS	Key management
Audit	Cloud Audit Logs	Activity tracking

Cost Optimization

Service	Cost Strategy
Cloud Run	Scale to zero, min instances = 0
Cloud Functions	Pay per invocation, <100ms = cheap
BigQuery	Use partitioned tables, set slot reservations
Cloud Storage	Lifecycle rules, nearline/coldline tiers
Cloud SQL	Use smallest instance, stop dev instances at night

Best Practices

✅ Use Workload Identity Federation (no service account keys) ✅ Implement idempotent event handlers (Pub/Sub at-least-once delivery) ✅ Use dead letter queues for failed messages ✅ Enable structured logging for Cloud Logging integration ✅ Tag all resources with environment, team, cost-center ✅ Use Terraform modules for reusable infrastructure ✅ Set budget alerts at 50%, 80%, 100%

Anti-Patterns

❌ Service account key files (use Workload Identity) ❌ Over-provisioned always-on GKE for simple APIs (use Cloud Run) ❌ Storing secrets in environment variables ❌ Missing dead letter queues on Pub/Sub subscriptions ❌ No retry policy on Pub/Sub subscriptions ❌ Broad IAM roles (roles/editor, roles/owner)

Example Prompts

• "Design an event-driven data pipeline on GCP with Pub/Sub and BigQuery"
• "Create Terraform modules for a Cloud Run service with Pub/Sub trigger"
• "Optimize this GCP architecture for cost and security"
• "Set up a dead-letter queue pattern for Pub/Sub message processing"

Related Skills

• GCP Architect Agent
• DevOps Agent
• Cloud-Native Architecture
• Security Best Practices
• Microservices Architecture
• DevOps & CI/CD